IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i9p387-d1469134.html
   My bibliography  Save this article

Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession

Author

Listed:
  • Leakey Omolo

    (Department of Mathematics and Statistics, Youngstown State University, Cafaro Hall, Youngstown, OH 44555, USA
    These authors contributed equally to this work.)

  • Nguyet Nguyen

    (Department of Mathematics and Statistics, Youngstown State University, Cafaro Hall, Youngstown, OH 44555, USA
    These authors contributed equally to this work.)

Abstract

The COVID-19 pandemic and the current wars in some countries have put incredible pressure on the global economy. Challenges for the U.S. include not only economic factors, major disruptions, and reorganizations of supply chains, but also those of national security and global geopolitics. This unprecedented situation makes predicting economic crises for the coming years crucial yet challenging. In this paper, we propose a method based on various machine learning models to predict the probability of a recession for the U.S. economy in the next year. We collect the U.S.’s monthly macroeconomic indicators and recession data from January 1983 to December 2023 to predict the probability of an economic recession in 2024. The performance of the individual economic indicator for the coming year was predicted separately, and then all of the predicted indicators were used to forecast a possible economic recession. Our results showed that the U.S. will face a high probability of being in a recession period in the last quarter of 2024.

Suggested Citation

  • Leakey Omolo & Nguyet Nguyen, 2024. "Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession," JRFM, MDPI, vol. 17(9), pages 1-26, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:387-:d:1469134
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/9/387/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/9/387/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Travis J. Berge, 2015. "Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 455-471, September.
    2. Andreas Psimopoulos, 2020. "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 18(1), pages 40-99.
    3. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seulki Chung, 2023. "Real-time Prediction of the Great Recession and the Covid-19 Recession," Papers 2310.08536, arXiv.org, revised May 2024.
    2. Borio, Claudio & Drehmann, Mathias & Xia, Fan Dora, 2020. "Forecasting recessions: the importance of the financial cycle," Journal of Macroeconomics, Elsevier, vol. 66(C).
    3. Leung, Charles Ka Yui & Ng, Joe Cho Yiu, 2018. "Macro Aspects of Housing," MPRA Paper 93512, University Library of Munich, Germany.
    4. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    5. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    6. Gregory de Walque & Thomas Lejeune & Ansgar Rannenberg, 2023. "Empirical DSGE model evaluation with interest rate expectations measures and preferences over safe assets," Working Paper Research 433, National Bank of Belgium.
    7. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    8. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    9. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    10. B. De Backer & M. Deroose & Ch. Van Nieuwenhuyze, 2019. "Is a recession imminent? The signal of the yield curve," Economic Review, National Bank of Belgium, issue i, pages 69-93, June.
    11. Davig, Troy & Hall, Aaron Smalter, 2019. "Recession forecasting using Bayesian classification," International Journal of Forecasting, Elsevier, vol. 35(3), pages 848-867.
    12. Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
    13. Filip Bašić & Tomislav Globan, 2023. "Early bird catches the worm: finding the most effective early warning indicators of recessions," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 36(1), pages 2120040-212, December.
    14. Rademacher, Philip, 2024. "Forecasting recessions in Germany with machine learning," DICE Discussion Papers 416, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    15. Heiberger, Raphael H., 2018. "Predicting economic growth with stock networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 102-111.
    16. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    17. Min Jeong Kim & Dohyoung Kwon, 2023. "Dynamic asset allocation strategy: an economic regime approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(2), pages 136-147, March.
    18. Okimoto, Tatsuyoshi & Takaoka, Sumiko, 2022. "The credit spread curve distribution and economic fluctuations in Japan," Journal of International Money and Finance, Elsevier, vol. 122(C).
    19. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    20. Jiayan YU & Jingqian ZHANG & Hee Eun SHIN & Jooan KONG, 2019. "Revisiting the Economic Crisis after a Decade: Statistical and Machine Learning Perspectives," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 14-19.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:387-:d:1469134. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.